Research map / Label-efficient and robust learning / Domain adaptation and generalization
Towards Unsupervised Domain Generalization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Pretrain with DARLING instead of SimCLR V2 | ResNet-18 · DomainNet all correlated, 1% labels | Overall accuracy: 11.80 -> 13.29 | Helped |
| Pretrain with DARLING instead of SimCLR V2 | ResNet-18 · PACS all correlated, 1% labels | Avg. accuracy: 25.93 -> 26.16 | Helped |
| Pretrain with DARLING instead of SimCLR V2 | ResNet-18 · DomainNet domain correlated, 100 pretraining categories | Overall accuracy: 46.50 -> 52.19 | Helped |
| Use adversarially generated domain-specific negatives instead of raw contrastive negatives (ablation) | DARLING · DomainNet domain correlated, 100 pretraining categories | Overall accuracy: 43.67 -> 49.57 | Helped |
| Add the similarity prediction network to domain-specific negative sampling (ablation) | DARL-st · DomainNet domain correlated, 100 pretraining categories | Overall accuracy: 49.57 -> 52.19 | Helped |
Closest papers by meaning
- Minimal Semantic Sufficiency Meets Unsupervised Domain Generalization (NeurIPS 2025 · similarity 0.92)
- Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap (ICLR 2025 · similarity 0.91)
- Towards Generalizing to Unseen Domains with Few Labels (CVPR 2024 · similarity 0.90)
- Is Large-scale Pretraining the Secret to Good Domain Generalization? (ICLR 2025 · similarity 0.90)
- Unknown Domain Inconsistency Minimization for Domain Generalization (ICLR 2024 · similarity 0.90)
- Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization (CVPR 2025 · similarity 0.90)
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